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Impulsivity interferes with emotion regulation strategy prioritization in everyday life
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Impulsivity interferes with emotion regulation strategy prioritization in everyday life
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Copyright 2023 Ellie Pin Xu
IMPULSIVITY INTERFERES WITH EMOTION REGULATION STRATEGY PRIORITIZATION
IN EVERYDAY LIFE
by
Ellie Pin Xu, B.A.
A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS, AND SCIENCES
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF ARTS
( PSYCHOLOGY)
May 2023
ii
Acknowledgments
I am extremely grateful to my advisor, Dr. Jonathan Stange, for all his support and
mentorship throughout the past two years. This project would not have been possible without
his encouragement and guidance. I would also like to thank my committee members, Dr.
Christopher Beam and Dr. Arthur Stone, for all their helpful and thoughtful feedback throughout
this process.
Last but not least, I would like to thank my family: my mother (Dr. Pin-Xian Xu), my sister
(soon-to-be Dr. Chelsea Xu), and my partner (Brady Grundy). I would not be where I am today
without them and am ever grateful for their endless love, support, and encouragement.
iii
Table of Contents
Acknowledgments................................................................................................................ ii
List of Tables........................................................................................................................ iv
List of Figures…................................................................................................................... v
Abstract................................................................................................................................ vi
Chapter 1: Introduction......................................................................................................... 1
Chapter 2: Method…….........................................................................................................
2.1. Participants and Procedure…............................................................................
6
6
2.2. Measures….............................................................................…....................... 7
2.3. Statistical Analyses…..............................................................…....................... 9
Chapter 3: Results.……........................................................................................................ 11
3.1. Descriptive Statistics…...................................................................................... 11
3.2. Level 2 Mediation Findings…...............................................................….......... 12
3.3. Level 1 Mediation Findings…...............................................................….......... 17
3.4. Group Differences in Strategy Prioritization....................................................... 18
3.5. Emotion Regulation Strategy Type as a Predictor of Strategy Prioritization...... 19
Chapter 4: Discussion……................................................................................................... 23
4.1. Conclusion......................................................................................................... 27
References………................................................................................................................ 28
iv
List of Tables
Table 1. Demographic and Clinical Characteristics of the Sample………............................ 11
Table 2. Dimensional Associations between Ecological Momentary Assessment
Measures…............................................................................................................
12
Table 3. Multilevel Mediation Model with Impulsivity as Predictor, Strategy Prioritization
as Mediator, and Regulation Success as Outcome……………………..…….…….
14
Table 4. Multilevel Mediation Model with Sensitivity Analysis (Impulsivity as Predictor,
Strategy Prioritization as Mediator, Average Regulatory Effort as Covariate, and
Regulation Success as Outcome……….................................................................
16
Table 5. Multilevel Model Predicting Strategy Prioritization with Depression History…….... 18
Table 6. Multilevel Model Predicting Strategy Prioritization with Depression History and
Average Regulatory Effort as a Sensitivity Analysis...............................................
19
Table 7. Multilevel Model Predicting Strategy Prioritization with Emotion Regulation
Strategy Type.........................................................................................................
21
v
List of Figures
Figure 1. Multilevel Mediation Model.………........................................................................ 13
Figure 2. Multilevel Mediation Model with Sensitivity Analysis……….................................. 15
Figure 3. Multilevel Model with Emotion Regulation Strategy Type Predicting Strategy
Prioritization………................................................................……….....................
20
vi
Abstract
Background: Prioritizing use of certain emotion regulation (ER) strategies over others has been
shown to predict well-being; however, it is unclear what mechanisms underlie this process.
Impulsivity, which captures both top-down control of and bottom-up reactivity to emotions, is one
potential mechanism of interest.
Methods: Using multilevel modeling, we investigated whether lower impulsivity predicts greater
ER strategy prioritization (i.e., greater between-strategy ER variability), and whether this
subsequently predicts greater ER success in individuals with remitted depression or no mental
disorder (N=82 participants with 1558 observations).
Results: The indirect effect of impulsivity on ER success was significant at the within-person
level (β=−.04, 95% CI = [−.08, −.01]) and the between-person level (β=−.18, 95% CI = [−.41,
−.02]). Specifically, in moments when individuals behaved less impulsively than usual, they
used ER strategies more variably than usual (β=−.06, 95% CI = [−.10, −.02], p<.007), which was
associated with more successful ER (β=.66, 95% CI = [.39, .92], p<.001). Individuals who, on
average, behaved less impulsively used ER strategies more variably (β=−.16, 95% CI = [−.29,
−.03], p<.02), which was also associated with greater ER success (β=1.12, 95% CI = [.37, 1.87],
p<.004).
Conclusion: ER strategy prioritization mediated the relationship between low impulsivity and
successful ER at two levels of analysis. Consequently, interventions focused on training
individuals to prioritize certain regulatory strategies over others may improve ER success,
particularly in moments when individuals are behaving more impulsively than usual.
Keywords: emotion regulation; impulsivity; multilevel modeling; ecological momentary
assessment; depression
1
Impulsivity interferes with emotion regulation strategy prioritization
in everyday life
Chapter 1: Introduction
Emotion regulation (ER) refers to a process in which individuals attempt to influence
their affective state. ER has been thought to encompass three stages: (1) the identification
stage, in which individuals identify their emotions and decide whether to regulate them; (2) the
selection stage, in which individuals select which ER strategies to use; and (3) the
implementation stage, in which individuals determine how to implement the selected strategies
(Gross, 2015). Particularly, in the selection stage, individuals must decide whether to prioritize
using certain ER strategies over others. The degree to which individuals prioritize certain
strategies over others has been operationalized as between-strategy variability (measured as
the standard deviation across ER strategies) (Double et al., 2022). Specifically, higher between-
strategy variability has been thought to indicate that an individual is either exclusively using one,
or a few, ER strategies (Blanke et al., 2020; Double et al., 2022). On the contrary, lower-
between strategy variability has been suggested to indicate that an individual is not prioritizing
one, or a few, ER strategies, but is instead using all ER strategies to a similar degree (Blanke et
al., 2020; Double et al., 2022).
Measuring how ER fluctuates over time, such as with between-strategy ER variability,
contrasts with traditional approaches to examining emotion regulation, which focus on average
levels of ER strategy use. Traditionally, the literature on emotion regulation has conceptualized
particular ER strategies as either adaptive or maladaptive. However, more recently, there has
been a shift towards focusing on how ER strategy use varies over time, as recent studies have
begun to suggest that whether an ER strategy is adaptive or maladaptive may depend on how
effectively it meets situational demands, rather than the inherent nature of the strategy itself
(Aldao, 2013; Aldao et al., 2015; Gross, 2015). For instance, reappraisal, which has traditionally
been considered an adaptive ER strategy, can either have a positive or negative impact on an
2
individual’s mood, depending on the context in which it is used (Ford & Troy, 2019; Haines et
al., 2016; Troy et al., 2013). Similarly, rumination, which has often been thought of as a
maladaptive ER strategy, has been shown to have adaptive qualities as well (Arditte &
Joormann, 2011; Joormann et al., 2006).
Recent work, focused on investigating between-strategy variability, has found that
greater between-strategy variability (i.e., greater strategy prioritization) predicts positive
outcomes, such as more successful regulation and greater affective well-being (Blanke et al.,
2020; Wang et al., 2021; Wenzel et al., 2021a; Wenzel et al., 2021b). Greater strategy
prioritization might be advantageous, in that it may indicate that an individual is selectively
choosing which ER strategies to use and persisting in their use of the selected strategies to
meet contextual demands (Aldao et al., 2015). On the contrary, lower strategy prioritization
might be disadvantageous, in that it may indicate that an individual is not selective in their use of
ER strategies. Instead, an individual showing low strategy prioritization might quickly switch
between (rather than persist in their use of) certain ER strategies. Thus, such an individual
might end up using many ER strategies to a similar extent, but no single strategy long enough to
lead to successful regulation.
Despite the beneficial outcomes associated with greater strategy prioritization, it remains
unclear what mechanisms contribute to strategy prioritization in daily life. Thus far, one study
has found support that emotional intelligence may contribute to strategy prioritization (Double et
al., 2022), and two studies have suggested that self-control may also be implicated in strategy
prioritization (Wenzel et al., 2021a; Wenzel et al., 2021b). In contrast with emotional intelligence
and self-control, here we aim to investigate whether impulsivity might interfere with strategy
prioritization.
Whereas self-control encompasses top-down aspects of self-regulation such as
response inhibition, impulsivity has been theorized to reflect the interaction of top-down
processes (e.g., response inhibition) with bottom-up processes (e.g., delay discounting) (Nigg,
3
2017). In other words, when individuals act impulsively, they are not only acting in an
uninhibited way, but also weighting immediate rewards more highly than delayed rewards.
Deficits in inhibition (Joormann & Vanderlind, 2014) and greater delay discounting (i.e., greater
weighting of immediate rewards over delayed rewards) (Malesza, 2021) both have been
associated with difficulties with effective ER and with depression. Thus, examining impulsivity,
which encompasses aspects of both response inhibition and delay discounting, may be
particularly relevant to understanding what contributes to (or interferes with) strategy
prioritization, and thus ultimately impacts successful ER. Specifically, impulsivity could prevent
individuals from thoughtfully selecting a strategy to regulate their emotions based on contextual
demands, and instead lead them to regulate their emotions in whatever way is easiest and
requires the least cognitive effort. It is also possible that impulsivity could lead individuals to
switch from one ER strategy to another, without persisting in using a single strategy long
enough to effectively regulate their emotions. Therefore, we expected that impulsivity would
predict strategy prioritization, and thus ER success, at both the between-person and within-
person levels. Specifically, we hypothesized that: (1) people with greater impulsivity, on
average, would show less strategy prioritization, and in turn less ER success, and (2) in
moments when people behave more impulsively compared to usual, they would also show less
strategy prioritization and thus less ER success compared to usual.
In the present study, we examined evidence from a clinical sample, which included
individuals with remitted depression and healthy comparison participants. Recent research on
between-strategy variability primarily has involved non-clinical samples consisting of
undergraduate students (Blanke et al., 2020; Wang et al., 2021; Wenzel et al., 2021a; Wenzel et
al., 2021b). One prior study found that individuals who showed lower ER strategy prioritization
reported greater depressive symptoms (Wang et al., 2021), suggesting that clinical
characteristics, such as a history of depression, might influence the degree to which individuals
prioritize certain ER strategies over others. For example, individuals with a history of depression
4
tend to differ in their spontaneous use of emotion regulation strategies (Ehring et al., 2010; Liu &
Thompson, 2017), and also tend to experience less successful ER (Joormann et al., 2007), than
healthy individuals. However, it is not known whether individuals with a history of depression
differ in the degree to which they prioritize certain ER strategies over others compared to
healthy individuals. Furthermore, if individuals who have remitted from depression show less
strategy prioritization than healthy individuals, this could suggest that lower strategy
prioritization may be a trait-like vulnerability factor (or psychological scar) of depression.
Given that research on strategy prioritization is still fairly nascent (Blanke et al., 2020;
Double et al., 2022; Wang et al., 2021; Wenzel et al., 2021a; Wenzel et al., 2021b), it also
remains unclear how this process unfolds over time. Prior work has suggested that, when an
individual shows greater strategy prioritization, they may simply be successfully selecting and
implementing certain ER strategies off the bat, such that using other ER strategies is not
necessitated (Blanke et al., 2020). To test this hypothesis, we examined whether the particular
types of ER strategies used predicted strategy prioritization. If particular types of ER strategies
that have previously shown to contribute to successful ER (e.g., reappraisal: Lennarz et al.,
2019) predict greater strategy prioritization, this could indicate that the relationship between
strategy prioritization and ER success may be a reciprocal process (Blanke et al., 2022;
Colombo et al., 2021). Specifically, successful ER may allow for greater strategy prioritization,
which then may further facilitate ER success. Examining whether the particular types of ER
strategies used impact strategy prioritization is an important first step in understanding how
strategies are being used when certain strategies are being prioritized over others. This also
serves as an important step in integrating existing literature on average levels of ER strategy
use, with more recent literature on variability in strategy use.
In summary, the present study first investigated whether (1) at the between-person level,
people who tended to act less impulsively showed greater ER strategy prioritization, and thus
greater ER success, and whether (2) at the within-person level, when individuals tended to act
5
less impulsively compared to usual, they showed greater ER strategy prioritization than usual,
and thus greater ER success. We investigated how variables were related at both the between-
and within-person levels, to improve our understanding of what interventions would be most
useful for (1) helping people who need it the most, and (2) helping in the moments when it is
needed the most (Fisher et al., 2018). Second, we examined, at the between-person level,
whether individuals with remitted depression tended to show lower strategy prioritization than
healthy individuals. Lastly, we determined, at both the between-person and within-person levels,
how the types of emotion regulation strategies used impacted strategy prioritization.
6
Chapter 2: Method
2.1. Participants and Procedure
The University of Illinois at Chicago (UIC) and University of Southern California (USC)
Institutional Review Boards approved this study. Participants were recruited via online
advertisements and university mailing lists and provided written informed consent. The sample
consisted of 44 individuals with remitted depression and 38 healthy comparisons, who were all
between the ages of 18 and 30 years old. Individuals with remitted depression were
considered eligible for the study if they: (1) met criteria for lifetime major depressive disorder
(MDD) (American Psychiatric Association, 2013), (2) had remitted from a depressive episode
for at least 2 months, and (3) scored lower than 8 on the 17-item Hamilton Depression Rating
Scale (HDRS-17) (Hamilton, 1960). Healthy individuals were considered eligible for the study if
they: (1) did not meet criteria for lifetime history of any psychiatric disorders, and (2) scored
lower than 8 on the HDRS-17. Diagnostic history was determined using the Diagnostic
Interview for Genetic Studies (DIGS) (Nurnberger et al., 1994), which was administered by a
trained research coordinator or graduate student, along with the HDRS-17.
Once participants provided consent to participate in the study, we trained participants on
the ecological momentary assessment (EMA) portion of our study, to ensure that participants
understood the meaning of each of the EMA items in our study. For EMA, we sent participants
six surveys each day, over the course of a week. Surveys were sent in pairs, with a “pre” and
“post” survey, once in the morning, afternoon, and evening. Each “post” survey was sent 30
minutes after its respective “pre” survey was completed. Participants chose to either receive
surveys via email or text, from either 8:00am to 7:00pm or from 10:00am to 9:00pm each day.
Participants were given an hour to complete each survey, and reminders were sent every 20
minutes over the course of the hour until each survey was completed. The present study
consisted of secondary analyses of existing data and focused on data collected specifically in
the “post” survey. On average, participants completed 19 out of the total 21 “post” surveys
7
(90.48% completion rate). The final sample included 82 participants with a total of 1558 “post”
surveys used in all analyses.
2.2. Measures
Emotion Regulation Strategies (Level 1)
ER strategy use was assessed using items from the Spontaneous Affect Regulation
Scale (SARS) (Egloff et al., 2006; Gruber et al., 2012; Stange et al., 2017). In each “post”
survey, participants reported their use of acceptance (2 items; ω t = .66), decentering (2 items; ω t
= .91), distraction (2 items; ωt = .72), mind-wandering (3 items; ωt = .88), reappraisal (3 items; ωt
= .84), and rumination (3 items; ω t = .87) since the “pre” survey about 30 minutes ago (Hayes &
Coutts, 2020). Participants were told to rate their use of each strategy on a scale of 1 (not at all)
to 10 (very much), specifically in response to any negative thoughts or feelings. To create a
subscale score for each of the six ER strategies, we averaged the item responses for each ER
strategy in each “post” survey.
For acceptance, decentering, distraction, and reappraisal, participants could also
respond with a “not applicable” option, if they had not experienced any negative thoughts or
feelings to regulate. The “not applicable” option was added to avoid ambiguity in low ratings, so
that low ratings would represent when ER strategies were not used in the presence of negative
thoughts or feelings (rather than when ER strategies were not used due to the absence of
negative thoughts or feelings). “Not applicable” responses were considered missing data. Of the
1558 survey pairs, the number of “post” surveys that were missing data on each respective ER
strategy was as follows: 999 (64.12%) for acceptance, 1004 (64.44%) for decentering, 1107
(71.05%) for distraction, 19 (1.22%) for mind-wandering, 1054 (67.65%) for reappraisal, and 17
(1.09%) for rumination.
Emotion Regulation Strategy Prioritization (Level 1)
Consistent with prior work (Bahlinger et al., 2022; Battaglini et al., 2022; Blanke et al.,
2020; Double et al., 2022; Wang et al., 2021), we computed between-strategy variability to
8
represent ER strategy prioritization, by calculating the standard deviation of the six ER strategy
subscale scores in each “post” survey. As a sensitivity analysis, we included mean regulatory
effort (measured at Level 1 as the mean of the six ER strategy subscale scores in each “post”
survey) as a covariate in several analyses, to capture variability in ER strategy use above and
beyond what could be attributed to mean ER strategy use. This was consistent with the
approach taken in with prior work (Blanke et al., 2020; Ebner-Priemer et al., 2009; Koval et al.,
2013).
Emotion Regulation Success (Level 1)
Perceived ER success was assessed at each “post” survey, with the item “I was
successful at regulating my emotions” from the SARS (Egloff et al., 2006; Gruber et al., 2012).
This item specifically was asked with respect to the period since the “pre” survey about 30
minutes ago. Responses were rated on a scale of 1 (not at all) to 10 (very much), with a “not
applicable” option, to be chosen if participants had not engaged in emotion regulation since the
“pre” survey. Of the 1558 survey pairs, 860 (55.20%) “post” surveys were missing data on
emotion regulation success.
Momentary Impulsivity (Level 1)
Momentary impulsivity was assessed using four items from the Momentary Impulsivity
Scale (MIS) (Tomko et al., 2014). These items were: (1) I said things without thinking, (2) I spent
more money than I meant to, (3) I have felt impatient, and (4) I made a “spur of the moment”
decision (ω t = .85) (Hayes & Coutts, 2020). Participants rated these items on a scale of 1 (not at
all) to 10 (very much). Mean momentary impulsivity was computed by averaging across all four
item responses at each observation. This item was asked in each “post” survey, with respect to
the period since the last “post” survey around four hours ago. Of the 1558 survey pairs, 34
(2.18%) “post” surveys were missing data on impulsivity.
9
2.3. Statistical Analyses
All analyses were conducted using R version 4.1.1. Independent samples t-tests were
used to compare healthy individuals to individuals with remitted MDD on demographic and
clinical characteristics. With 1558 observations nested within 82 participants, we performed a
series of multilevel models using the lme function in the nlme package in R (Pinheiro et al.,
2017) with a restricted maximum likelihood method of estimation (REML). First, we fitted a level
1-1-1 mediation model predicting ER success with momentary impulsivity as a predictor and
between-strategy variability as a mediator. As a sensitivity analysis, we also fit this level 1-1-1
mediation model including mean regulatory effort as a Level 1 covariate. Second, we fitted a
multilevel model predicting between-strategy variability (Level 1 outcome variable), using
depression history (Level 2 predictor). Similarly, as a sensitivity analysis, we also fit this
multilevel modeling including mean regulatory effort as a Level 1 covariate. Lastly, we fitted a
multilevel model predicting between-strategy variability (Level 1 outcome variable), using each
distinct ER strategy (e.g., acceptance, decentering, distraction, mind-wandering, reappraisal,
and rumination) separately as a predictor (Level 1 predictors). In this multilevel model, we did
not include mean regulatory effort as a covariate, given that mean regulatory effort was
calculated by taking the mean of acceptance, decentering, distraction, mind-wandering,
reappraisal, and rumination in each “post” survey at Level 1.
To disentangle between-person and within-person effects, the mean of each Level 1
predictor was included in all models. We also specified random slopes for each model, to
account for individual differences in effects of each Level 1 predictor (Snijders & Bosker, 2011).
Given unequal time intervals between surveys, we specified a continuous autoregressive error
structure (Snijders & Bosker, 2011). Accounting for the multilevel data structure, we imputed
missing data for each item using the predictive mean matching algorithm in the mice package in
R (Eekhout et al., 2014; Enders, 2017; Gottschall et al., 2012; Van Buuren & Groothuis-
Oudshoorn, 2011). We generated 100 imputed datasets and reported findings from multilevel
10
models based on pooled analyses of these datasets. We used multiple imputation to account for
missing data, given that using complete case analysis can bias findings if the complete cases
are not representative of the entire sample (Pedersen et al., 2017). Using multiple imputation
allowed us to utilize all available information in the dataset to account for missing data and
preserve relationships between variables (Pedersen et al., 2017).
We reported all coefficient estimates, 95% confidence intervals (CIs), and effect sizes
(marginal R
2
) of the multilevel models. Coefficient estimates were interpreted to be significant if
the probability was below an alpha value of .05. The 95% CI of the indirect effect in the
multilevel mediation model was determined using Monte Carlo simulation, with 20000
simulations, which has been shown to not only perform as well as other methods (e.g.,
bootstrap), but also be less computationally intensive (Preacher & Selig, 2012; Selig &
Preacher, 2008; Tofighi & MacKinnon, 2016).
11
Chapter 3: Results
3.1. Descriptive Statistics
We reported descriptive statistics of the sample’s demographic and clinical
characteristics in Table 1. Compared to healthy individuals, individuals with remitted MDD, on
average, showed lower average regulatory success, lower average use of decentering, and
greater average use of mind-wandering (Table 1). The percentage of Asians included in the
sample of healthy volunteers was also significantly higher than the percentage of Asians
included in the sample of individuals with remitted depression (Table 1). Otherwise, the groups
did not differ on demographic and clinical characteristics (Table 1). We also reported
correlations between Level 2 ecological momentary assessment measures in Table 2. We
found that greater strategy prioritization at Level 2 was related to greater average regulatory
effort, greater regulatory success, greater decentering, greater reappraisal, lower momentary
impulsivity, and lower brooding at Level 2 (Table 2).
Table 1. Demographic and clinical characteristics of the sample
Healthy Volunteers
(N=38)
Individuals with
Remitted MDD
(N=44)
Age, mean (SD) 25.48 (3.95) 26.88 (3.72)
Female (%) 63.16 65.91
Race
White (%) 31.58 52.27
Black (%) 13.16 15.91
Latino (%) 10.53 11.36
Asian (%) 31.58
a
11.36
b
Multiracial (%) 13.16 6.82
Other (%) 0.00 2.27
Education Level 7.75 7.95
Strategy Prioritization, mean (SD) 2.23 (.74) 1.98 (.61)
Average Regulatory Effort, mean (SD) 4.45 (1.03) 4.18 (1.09)
12
Abbreviations: MDD, major depressive disorder; SD, standard deviation.
Note: For education level, a score of 7 is equivalent to an Associate’s degree and a score of 8 is equivalent to a
Bachelor’s degree.
a, b Different superscripts within rows indicate significant pairwise comparisons between groups (p<.05).
Table 2. Dimensional associations between ecological momentary assessment measures
Abbreviations: SD, standard deviation.
Intraclass correlations ranged across imputed datasets, indicating that variability at the
person-level accounted for at least 38.26% and up to 67.66% of the total variability in strategy
prioritization, and at least 35.28% and up to 36.21% of the total variability in regulatory success,
thus justifying the use of a multilevel modeling approach in subsequent analyses.
3.2. Level 2 Mediation Findings
At the between-person level, do people who tend to act less impulsively on average show
greater strategy prioritization, and in turn, greater success with emotion regulation?
The indirect effect of impulsivity on ER success was significant at the between-person
level (β=−.18, 95% CI = [−.41, −.02]; Figure 1). Specifically, individuals who, on average,
behaved less impulsively showed greater strategy prioritization (β=−.16, SE=.07, 95% CI =
[−.29, −.03], p<.02; Table 3, Figure 1). Further, individuals who showed greater strategy
1 2 3 4 5 6 7 8 9 Mean SD Range Possible Range
1 Strategy Prioritization (Mean) ----- 2.08 0.67 0.71 - 3.97 0.00 - 4.93
2 Average Regulatory Effort (Mean) .30** ----- 4.29 1.07 1.88 - 6.74 1.00 - 10.00
3 Regulatory Success (Mean) .40** .40** ----- 6.80 1.89 2.46 - 10.00 1.00 - 10.00
4 Momentary Impulsivity (Mean) -.35** .31** -.19 ----- 1.88 0.85 1.00 - 5.29 1.00 - 10.00
5 Acceptance (Mean) .18 .46** .23 .21 ----- 4.85 1.80 1.00 - 8.67 1.00 - 10.00
6 Brooding (Mean) -.24* .56** -.15 .55** .21 ----- 2.22 1.20 1.00 - 6.33 1.00 - 10.00
7 Decentering (Mean) .48** .62** .69** -.12 .44** -.03 ----- 5.21 2.13 1.00 - 10.00 1.00 - 10.00
8 Distraction (Mean) .15 .65** .17 .14 .01 .30** .26* ----- 3.78 1.73 1.00 - 9.00 1.00 - 10.00
9 Mind-Wandering (Mean) -.18 .46** -.20 .65** .08 .50** -.20 .30** ----- 3.41 1.63 1.09 - 7.11 1.00 - 10.00
10 Reappraisal (Mean) .37** .64** .40** -.04 .04 .16 .48** .52** .02 4.88 1.71 1.00 - 10.00 1.00 - 10.00
Momentary Impulsivity, mean (SD) 1.79 (.91) 1.95 (.80)
Regulatory Success, mean (SD) 7.51 (1.89)
a
6.24 (1.72)
b
Acceptance, mean (SD) 5.04 (1.84) 4.71 (1.78)
Brooding, mean (SD) 2.11 (1.21) 2.32 (1.20)
Decentering, mean (SD) 6.05 (2.31)
a
4.58 (1.77)
b
Distraction, mean (SD) 3.78 (1.95) 3.78 (1.57)
Mind-Wandering, mean (SD) 2.94 (1.53)
a
3.81 (1.63)
b
Reappraisal, mean (SD) 5.27 (1.84) 4.59 (1.57)
13
prioritization, on average, showed greater ER success (β=1.12, SE=.38, 95% CI = [.37, 1.87],
p<.004; Table 3, Figure 1). The direct effect of impulsivity on ER success was not significant at
the between-person level, suggesting that ER strategy prioritization fully mediated the
relationship between impulsivity and regulatory success at Level 2. Specifically, individuals who,
on average, behaved less impulsively showed no difference in ER success (p=.292; Table 3,
Figure 1), after covarying for ER strategy prioritization.
Figure 1. Multilevel mediation model
Abbreviations: 95% CI, 95% confidence interval.
14
Table 3. Multilevel mediation model with impulsivity as predictor, strategy prioritization as mediator,
and regulation success as outcome
Outcome: Regulation Success
Estimate Standard Error 95% CI p
Fixed effects
Intercept 3.65 0.98 [1.74, 5.56] 0.000
Strategy Prioritization 0.66 0.14 [0.39, 0.92] 0.000
Strategy Prioritization (person-mean) 1.12 0.38 [0.37, 1.87] 0.003
Momentary Impulsivity (person-mean
centered)
−0.26 0.08 [−0.42, −0.09] 0.002
Momentary Impulsivity (person-mean) −0.22 0.21 [−.64, 0.19] 0.292
Random effects
𝜏 0
2
(Intercept) 3.23
𝜏 1
2
(Strategy Prioritization) 0.39
𝜏 2
2
(Momentary Impulsivity [person-
mean centered])
0.07
𝜏 01 −0.72
𝜏 02 0.25
𝜏 12 −0.14
σ
2
3.30
ICC 0.35
Marginal R
2
0.17
Outcome: Strategy Prioritization
Estimate Standard Error 95% CI p
Fixed effects
Intercept 2.45 0.14 [2.17, 2.73] 0.000
Momentary Impulsivity (person-mean
centered)
−0.06 0.02 [−0.10, −0.02] 0.006
Momentary Impulsivity (person-mean) −0.16 0.07 [−0.29, −0.03] 0.014
Random effects
𝜏 0
2
(Intercept) 0.21
𝜏 1
2
(Momentary Impulsivity [person-
mean centered])
0.00
𝜏 01 −0.06
σ
2
0.34
15
ICC 0.39
Marginal R
2
0.04
Abbreviations: 95% CI, 95% confidence interval.
After covarying for average regulatory effort, the indirect effect of impulsivity on ER
success remained significant at the between-person level (β=−.24, 95% CI = [−.51, −.04];
Figure 2). The effect of impulsivity on strategy prioritization (β=−.25, SE=.06, 95% CI = [−.37,
−.13], p<.001; Table 4, Figure 2), and the effect on strategy prioritization on ER success
(β=.97, SE=.41, 95% CI = [.17, 1.78], p<.02; Table 4, Figure 2) both remained significant at the
between-person level. With respect to average regulatory effort, we found that individuals who
showed greater average regulatory effort showed greater strategy prioritization (β=.45, SE=.07,
95% CI = [.32, .58], p<.001; Table 4, Figure 2), but not greater success with emotion regulation,
on average (p=.051; Table 4, Figure 2). ER strategy prioritization thus also fully mediated the
effect of average regulatory effort on regulatory success at the between-person level (Figure 2).
The estimated marginal R
2
of these multilevel models are reported in Table 3 and Table 4.
Figure 2. Multilevel mediation model with sensitivity analysis
Abbreviations: 95% CI, 95% confidence interval.
16
Table 4. Multilevel mediation model with sensitivity analysis (impulsivity as predictor, strategy
prioritization as mediator, average regulatory effort as covariate, and regulation success as
outcome)
Outcome: Regulation Success
Estimate Standard
Error
95% CI p
Fixed effects
Intercept 2.85 1.07 [0.76, 4.94] 0.008
Strategy Prioritization 0.49 0.14 [0.23, 0.76] 0.000
Strategy Prioritization (person-mean) 0.97 0.41 [0.17, 1.78] 0.018
Momentary Impulsivity (person-mean
centered)
−0.30 0.08 [−0.46, −0.14] 0.000
Momentary Impulsivity (person-mean) −0.45 0.23 [−0.91, 0.01] 0.056
Average Regulatory Effort (person-mean
centered)
0.40 0.12 [0.17, 0.63] 0.001
Average Regulatory Effort (person-mean) 0.47 0.24 [−0.00, 0.95] 0.051
Random effects
𝜏 0
2
(Intercept) 2.83
𝜏 1
2
(Strategy Prioritization) 0.40
𝜏 2
2
(Momentary Impulsivity [person-mean
centered])
0.07
𝜏 3
2
(Average Regulatory Effort [person-
mean centered])
0.13
𝜏 01 −0.70
𝜏 02 0.41
𝜏 03 0.20
𝜏 12 −0.28
𝜏 13 −0.45
𝜏 23 0.26
σ
2
3.16
ICC 0.36
Marginal R
2
0.20
Outcome: Strategy Prioritization
Estimate Standard
Error
95% CI p
Fixed effects
17
Intercept 0.82 0.26 [0.32, 1.32] 0.001
Momentary Impulsivity (person-mean
centered)
−0.07 0.02 [−0.12, −0.03] 0.001
Momentary Impulsivity (person-mean) −0.25 0.06 [−0.37, −0.13] 0.000
Average Regulatory Effort (person-mean
centered)
0.27 0.04 [0.20, 0.34] 0.000
Average Regulatory Effort (person-mean) 0.45 0.07 [0.32, 0.58] 0.000
Random effects
𝜏 0
2
(Intercept) 0.18
𝜏 1
2
(Momentary Impulsivity [person-mean
centered])
0.00
𝜏 2
2
(Average Regulatory Effort [person-
mean centered])
0.06
𝜏 01 0.04
𝜏 02 0.64
𝜏 12 −0.07
σ
2
0.28
ICC 0.45
Marginal R
2
0.23
Abbreviations: 95% CI, 95% confidence interval.
3.3. Level 1 Mediation Findings
At the within-person level, in moments when individuals act less impulsively compared to usual,
do they show greater strategy prioritization, and in turn, greater success with emotion
regulation?
The indirect effect of impulsivity on ER success was significant at the within-person level
(β=−.04, 95% CI = [−.08, −.01]; Figure 1). Specifically, in moments when people behaved less
impulsively compared to usual, they used greater ER strategy prioritization than usual (β=−.06,
SE=.02, 95% CI = [−.10, −.02], p<.007; Table 3, Figure 1), which was associated with greater
ER success than usual (β=.66, SE=.14, 95% CI = [.39, .92], p<.001; Table 3, Figure 1). The
direct effect of impulsivity on ER success was also significant at the within-person level, given
that in moments when individuals acted less impulsively, they showed lower ER success
(β=−.26, SE=.08, 95% CI = [−.42, −.09], p<.003; Table 3, Figure 1).
18
After covarying for average regulatory effort, the indirect effect of impulsivity on ER
success remained significant at the within-person level (β=−.04, 95% CI = [−.07, −.01]; Figure
2). The effect of impulsivity on strategy prioritization (β=−.07, SE=.02, 95% CI = [−.12, −.03],
p<.001; Table 4, Figure 2), and the effect of strategy prioritization on ER success (β=.49,
SE=.14, 95% CI = [.23, .76], p<.001; Table 4, Figure 2), both also remained significant at the
within-person level. Additionally, the direct effect of impulsivity on ER success remained
significant (β=−.30, SE=.08, 95% CI = [−.46, −.14], p<.001; Table 4, Figure 2). In terms of
average regulatory effort, we found that, in moments when individuals showed more average
regulatory effort than usual, they also indicated more strategy prioritization than usual (β=.27,
SE=.04, 95% CI = [.20, .34], p<.001; Table 4, Figure 2) and more success with emotion
regulation compared to usual (β=.40, SE=.12, 95% CI = [.17, .63], p<.001; Table 4, Figure 2).
3.4. Group Differences in Strategy Prioritization
Do individuals with a history of depression show lower strategy prioritization than healthy
individuals?
Individuals with remitted depression showed lower strategy prioritization compared to
healthy individuals (β=−.24, SE=.11, 95% CI = [−.46, −.02], p<.04, R
2
= .03; Table 5). After
covarying for average regulatory effort, the effect of depression history on strategy prioritization
remained significant (β=−.20, SE=.10, 95% CI = [−.40, −.01], p<.05, R
2
= .20; Table 6).
Table 5. Multilevel model predicting strategy prioritization with depression history
Estimate Standard Error 95% CI p
Fixed effects
Intercept 2.28 0.09 [2.11, 2.45] 0.000
Depression History −0.24 0.11 [−.46, −.02] 0.034
Random effects
𝜏 0
2
(Intercept)
0.21
σ
2
0.34
ICC 0.38
19
Marginal R
2
0.03
Abbreviations: 95% CI, 95% confidence interval.
Table 6. Multilevel model predicting strategy prioritization with depression history and average
regulatory effort as a sensitivity analysis
Estimate Standard Error 95% CI p
Fixed effects
Intercept 0.83 0.28 [0.28, 1.38] 0.003
Depression History −0.20 0.10 [−.40, −.01] 0.041
Average Regulatory Effort
(person-mean)_
0.36 0.07 [0.23, 0.49] 0.000
Average Regulatory Effort
(person-mean centered)
0.26 0.04 [0.19, 0.33] 0.000
Random effects
𝜏 0
2
(Intercept)
0.22
𝜏 1
2
(Average Regulatory Effort
[person-mean centered])
0.06
𝜏 01 0.68
σ
2
0.28
ICC 0.48
Marginal R
2
0.20
Abbreviations: 95% CI, 95% confidence interval.
3.5. Emotion Regulation Strategy Type as a Predictor of Strategy Prioritization
How does the type of emotion regulation strategy used relate to strategy prioritization?
At the between-person level, individuals who, on average, used more acceptance
(β=.12, SE=.04, 95% CI = [.05, .20], p<.002; Table 7, Figure 3), decentering (β=.15, SE=.05,
95% CI = [.06, .24], p<.002; Table 7, Figure 3), or reappraisal (β=.17, SE=.06, 95% CI = [.06,
.29], p<.005; Table 7, Figure 3) to regulate their emotions showed greater strategy
prioritization. In contrast, individuals who, on average, used more rumination to regulate their
emotions showed lower strategy prioritization (β=−.14, SE=.05, 95% CI = [−.23, −.04], p<.005;
Table 7, Figure 3). There were no differences in strategy prioritization between individuals
20
based on their average use of distraction (p=.162; Table 7), and mind-wandering (p=.652;
Table 7).
At the within-person level, in moments when individuals used more acceptance (β=.09,
SE=.01, 95% CI = [.07, .12], p<.001; Table 7, Figure 3), decentering (β=.12, SE=.01, 95% CI =
[.09, .15], p<.001; Table 7, Figure 3), or reappraisal (β=.06, SE=.01, 95% CI = [.03, .08],
p<.001; Table 7, Figure 3) compared to usual, they tended to show greater strategy
prioritization compared to usual. On the contrary, in moments when people used more
rumination (β=−.10, SE=.02, 95% CI = [−.13, −.07], p<.001; Table 7, Figure 3) than usual, they
tended to show less strategy prioritization compared to usual. In moments when individuals
engaged in distraction (p=.054; Table 7) or mind-wandering (p=.381; Table 7) more than usual,
they did not show any difference in strategy prioritization compared to usual. The marginal R
2
of
this multilevel model was estimated to be 0.47 (Table 7).
Figure 3. Multilevel model with emotion regulation strategy type predicting strategy prioritization
Abbreviations: 95% CI, 95% confidence interval.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Acceptance
Strategy
Prioritization
β=.12
95% CI = [.05, .20]
Between-Person (Level 2)
+
Decentering
β=.15
95% CI = [.06, .24]
+
Rumination
-
β= −.14
95% CI = [ −.23, −.04]
Reappraisal
β=.17
95% CI = [.06, .29]
+
Acceptance
Strategy
Prioritization
β=.09
95% CI = [.07, .12]
Within-Person (Level 1)
+
Decentering
β=.12
95% CI = [.09, 15]
+
Rumination
-
β= −.10
95% CI = [ −.13, −.07]
Reappraisal
β=.06
95% CI = [.03, .08]
+
21
Table 7: Multilevel model predicting strategy prioritization with emotion regulation strategy type
Estimate Standard Error 95% CI p-value
Fixed effects
Intercept 0.56 0.25 [0.06 ,1.05] 0.027
Reappraisal (person-mean) 0.17 0.06 [0.06, 0.29] 0.004
Reappraisal (person-mean
centered)
0.06 0.01 [0.03, 0.08] 0.000
Acceptance (person-mean) 0.12 0.04 [0.05, 0.20] 0.002
Acceptance (person-mean
centered)
0.09 0.01 [0.07, 0.12] 0.000
Distraction (person-mean) −0.09 0.06 [−0.20, 0.03] 0.162
Distraction (person-mean
centered)
−0.03 0.01 [−0.05, 0.00] 0.054
Decentering (person-mean) 0.15 0.05 [0.06, 0.24] 0.002
Decentering (person-mean
centered)
0.12 0.01 [0.09, 0.15] 0.000
Mind-wandering (person-mean) −0.02 0.04 [−0.09, 0.06] 0.652
Mind-wandering (person-mean
centered)
−0.01 0.02 [−0.04, 0.02] 0.381
Rumination (person-mean) −0.14 0.05 [−0.23, −0.04] 0.005
Rumination (person-mean
centered)
−0.10 0.02 [−0.13, −0.07] 0.000
Random effects
𝜏 0
2
(Intercept)
0.13
𝜏 1
2
(Reappraisal [person-mean
centered])
0.01
𝜏 2
2
(Acceptance [person-mean
centered])
0.01
𝜏 3
2
(Distraction [person-mean
centered])
0.01
𝜏 4
2
(Decentering [person-mean
centered])
0.01
𝜏 5
2
(Mind-wandering [person-
mean centered])
0.01
𝜏 6
2
(Rumination [person-mean
centered])
0.01
𝜏 01 −0.24
𝜏 02 −0.17
𝜏 03
0.45
𝜏 04 −0.10
22
𝜏 05 0.25
𝜏 06 0.42
𝜏 12 −0.30
𝜏 13 −0.01
𝜏 14 −0.09
𝜏 15 −0.29
𝜏 16 −0.24
𝜏 23 −0.27
𝜏 24 0.30
𝜏 25 −0.31
𝜏 26 −0.01
𝜏 34 −0.12
𝜏 35 −0.06
𝜏 36 −0.14
𝜏 45 −0.48
𝜏 46 −0.19
𝜏 56 0.05
σ
2
0.12
ICC 0.68
Marginal R
2
0.47
Abbreviations: 95% CI, 95% confidence interval.
23
Chapter 4: Discussion
The present study found that, at both the between- and within-person levels, momentary
impulsivity was associated with emotion regulation strategy prioritization, which was also related
to emotion regulation success. We extended prior work, which showed that deficits in top-down
processes (e.g., self-control) relate to strategy prioritization (Wenzel et al., 2021a; Wenzel et al.,
2021b), by illustrating that impulsivity is also associated with strategy prioritization. Impulsivity
has been thought to capture both top-down (e.g., response inhibition) and bottom-up (e.g., delay
discounting) processes (Nigg, 2017). Given that impulsivity predicted strategy prioritization, the
present findings could suggest that difficulties in strategy prioritization might be driven by both
(1) weakened top-down control (e.g., difficulties with ignoring distractions to persist in ER
strategy use), and (2) heightened bottom-up reactivity to emotions (e.g., highly valuing an
immediate reduction in negative affect, and possibly switching quickly from one ER strategy to
the next).
Not only was impulsivity associated with greater strategy prioritization, but greater
strategy prioritization was associated with greater regulatory success. These findings build on
prior work, which has related strategy prioritization to positive clinical outcomes (Blanke et al.,
2020; Wang et al., 2021; Wenzel et al., 2021a; Wenzel et al., 2021b), by showing that strategy
prioritization may also be a mechanism by which reduced impulsivity leads to more positive
clinical outcomes (Carver & Johnson, 2018). Interventions focused on training individuals to
prioritize certain ER strategies over others may improve regulatory success, particularly in
moments when people are acting more impulsively than usual. Additionally, cognitive training
interventions that target response inhibition and working memory have been shown to decrease
impulsivity (Peckham & Johnson, 2018) – it is possible that such interventions may also benefit
strategy prioritization, by providing individuals with the skills to more effectively ignore
distractions in order to selectively use ER strategies (and persist in ER strategy use), and to
24
more effectively use ER strategies that may rely on working memory (e.g., reappraisal) (McRae
et al., 2012; Pe et al., 2013).
In the present study, mean strategy prioritization was approximately 2.08 on average,
which was comparable to previous studies that have reported mean strategy prioritization
ranging from 1.20 to 2.11 (when converted to a similar scale) (Blanke et al., 2020; Double et al.,
2022; Wang et al., 2021). Average regulatory effort was approximately 4.29 on average, which
was also comparable to previous studies that have reported average regulatory effort ranging
from 2.10 to 4.25 (when converted to a similar scale) (Blanke et al., 2020; Double et al., 2022;
Wang et al., 2021).
Individuals with remitted depression showed lower strategy prioritization compared to
healthy individuals – this suggests that lowered strategy prioritization may either be a
vulnerability factor for depression, or a psychological “scar” of depression. Future studies should
elucidate whether lowered strategy prioritization may confer vulnerability to depression, by
exploring whether individuals who are at risk for depression (e.g., first-degree relatives of
individuals with depression) show lower strategy prioritization compared to healthy individuals. If
individuals who are at risk of depression show lower strategy prioritization, this may suggest that
lowered strategy prioritization could be a vulnerability factor for depression. If individuals at risk
do not show lower strategy prioritization, this may suggest that lowered strategy prioritization
may not be a vulnerability factor for depression, and instead might be a psychological
consequence of depression. Much of the literature on strategy prioritization has been limited to
non-clinical samples thus far (Blanke et al., 2020; Wang et al., 2021; Wenzel et al., 2021a;
Wenzel et al., 2021b). Given that emotion dysregulation is relevant to many forms of
psychopathology, it remains important to elucidate the role that lowered strategy prioritization
may play in the development of psychopathology, using clinical and at-risk samples with larger
sample sizes.
25
Here, we found that acceptance, decentering, and reappraisal were associated with
greater strategy prioritization, whereas rumination was associated with lower strategy
prioritization. As suggested in prior work (Blanke et al., 2020), it is possible that greater strategy
prioritization may indicate the successful selection and implementation of certain ER strategies,
which sufficiently meet contextual demands without necessitating the use of other additional ER
strategies. In particular, acceptance, decentering, and reappraisal may tend to relate to
successful ER (Lennarz et al., 2019; Webb et al., 2012; Wu et al., 2022; Wylie et al., 2023),
such that additional ER strategies are not needed, allowing these selective few strategies to be
prioritized. On the contrary, rumination may tend to lead to unsuccessful ER (Aldao et al., 2010;
Lennarz et al., 2019; Wylie et al., 2023) - this may necessitate the use of additional ER
strategies, thus contributing to less selective ER strategy use. Given that strategy prioritization
and regulatory success could have a reciprocal relationship, future studies should explore these
constructs on a smaller timescale, to better understand how these processes contribute to one
another.
One limitation of the present study is that our EMA protocol captured overall ER strategy
use within an approximately 30-minute timeframe – thus, we were not able to conduct a more
fine-grained analysis on how ER strategies were used within this timeframe. It is possible that
individuals who showed low strategy prioritization could have either (1) quickly switched from
using one ER strategy to the next, (2) simultaneously used many ER strategies all at once
throughout this timeframe, or (3) refrained from using any ER strategies during this timeframe.
Thus, it remains unclear the mechanism by which reduced strategy prioritization relates to lower
regulatory success. Given that we asked participants to report their ER strategy use in 21
surveys (three surveys each day for seven days), we did not collect data on ER strategy use on
a smaller timescale to limit participant burden (Ebner-Priemer & Trull, 2009). Future studies
could examine between-strategy variability on a smaller timescale, to capture how strategy
prioritization unfolds over time in more nuance, and to better understand the directionality of the
26
relationship between strategy prioritization and regulatory success. One way that future studies
could achieve this is by asking participants to complete more surveys on a single day about ER
strategy use and ER success, but for fewer days. Future studies could also ask participants to
report not only the degree to which they engaged in using ER strategies, but also how long they
persisted in ER strategy use before switching strategies.
A second limitation of this study is the use of self-report data. Given that participants
were specifically asked to report their ER strategy use in response to unpleasant thoughts or
feelings, participants would have only endorsed using ER strategies if they perceived a need to
regulate their emotions. Individuals with depression have been suggested to differ in their
emotion preferences (Vanderlind et al., 2020), which might impact the circumstances in which
individuals with depression determine that there is a need to regulate their emotions. Given this,
our findings should be replicated and extended in future work, which could examine participants’
ER strategy use in response to a lab-based stressor. Future work would be important in
determining whether higher self-reported momentary impulsivity is related to lower strategy
prioritization, not only in moments when individuals perceive a need to regulate their emotions,
but also in the context of a lab-based stressor that is standardized across participants.
Lastly, the effect sizes found in this study are small. Impulsivity predicted approximately
4% of the variance in strategy prioritization, and together, impulsivity and strategy prioritization
predicted around 17% of the variance in regulation success (Table 3). Though these individual
effects were small, small effect sizes could still be important to predicting regulatory behavior
and outcomes. Future work can build on these findings and account for more of the variance in
these constructs by investigating how situational factors might shape strategy prioritization and
regulation success. This would be important to further capturing how context impacts ER
processes (Aldao, 2013).
27
4.1. Conclusion
This study is the first to document relationships between impulsivity and prioritization of
certain ER strategies. Examining the process of ER repeatedly over time allowed us to detect
these relationships between and within individuals in everyday life. Individuals who tended to act
less impulsively showed greater prioritization of certain ER strategies over others and also more
successful regulation. Further, during moments when individuals acted less impulsively than
usual, they showed greater prioritization of certain ER strategies over others, which was
associated with greater ER success. Interventions focused on training people to prioritize
certain ER strategies over others may improve regulation success, particularly in moments
when people are behaving more impulsively than usual.
28
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Abstract (if available)
Abstract
Background: Prioritizing use of certain emotion regulation (ER) strategies over others has been shown to predict well-being; however, it is unclear what mechanisms underlie this process. Impulsivity, which captures both top-down control of and bottom-up reactivity to emotions, is one potential mechanism of interest.
Methods: Using multilevel modeling, we investigated whether lower impulsivity predicts greater ER strategy prioritization (i.e., greater between-strategy ER variability), and whether this subsequently predicts greater ER success in individuals with remitted depression or no mental disorder (N=82 participants with 1558 observations).
Results: The indirect effect of impulsivity on ER success was significant at the within-person level (β=−.04, 95% CI = [−.08, −.01]) and the between-person level (β=−.18, 95% CI = [−.41, −.02]). Specifically, in moments when individuals behaved less impulsively than usual, they used ER strategies more variably than usual (β=−.06, 95% CI = [−.10, −.02], p<.007), which was associated with more successful ER (β=.66, 95% CI = [.39, .92], p<.001). Individuals who, on average, behaved less impulsively used ER strategies more variably (β=−.16, 95% CI = [−.29, −.03], p<.02), which was also associated with greater ER success (β=1.12, 95% CI = [.37, 1.87], p<.004).
Conclusion: ER strategy prioritization mediated the relationship between low impulsivity and successful ER at two levels of analysis. Consequently, interventions focused on training individuals to prioritize certain regulatory strategies over others may improve ER success, particularly in moments when individuals are behaving more impulsively than usual.
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Xu, Ellie Pin
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Impulsivity interferes with emotion regulation strategy prioritization in everyday life
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Psychology
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2023-05
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